Connected Cars: How Vehicle Sensors Will Report and Inform What's Ahead
【Summary】Automotive technology has come a long way in the past ten years. Soon your car will be able take you to work while you sit back and enjoy the ride or read a book. But while you’re relaxing on the road your car will be connected to the cloud to make sure it keeps you safe.
Automotive technology has come a long way in the past ten years. Soon your car will be able take you to work while you sit back and enjoy the ride or read a book. But while you're relaxing on the road your car will be connected to the cloud to make sure it keeps you safe.
To accomplish automated driving, a connected vehicle will use hundreds of onboard sensors, artificial intelligence (AI), machine learning, and a enormous amounts of computing power and off-board cloud services to make the best decisions on your behalf. In addition to what it can "see" using LiDAR, the vehicle will also need information about what is out of range of its sensors.
Connected cars will upload and retrieve this data to a cloud network, so other autonomous vehicles can access it in real-time.
Traditional maps lack the accuracy a self-driving car needs to navigate. Recently, there is an enormous amount of money being spent in the 3D or HD mapping space from companies such as HERE, which was started by Nokia and then acquired by the German automakers BMW, Daimler and Audi.
HERE is currently mapping roads in the United States and in Europe. With HD mapping, intersections are being mapped to centimeter resolution, and cameras, sonar, radar, and LiDAR and deep-learning are being used to differentiate pedestrians on the sidewalk, a bicyclist in the roadway and thousands of other critical identifications.
In addition to the navigational maps and close-proximity detection technologies autonomous vehicle functions will also be continuously cloud connected so that they can consider exception-based events happening ahead of them.
These events, or incidents, will come from either known datasets, such as a planned construction event that closes two of four lanes three miles ahead, known collisions, or a shared data library of data collected from other vehicles that travelled the same path a few minutes earlier. With HD mapping, an autonomous car can be ‘geofenced' to avoid areas of construction until it's completed.
Vehicle to Cloud Networks
Similar to pooled GPS data used today to perform live traffic rerouting directions, incident detection and transmission will serve a critical role in the safety of autonomous vehicles.
Vehicle-to-cloud (V2C) networks will learn where potholes are, know the locations and times of the day where there is increased pedestrian traffic, know about accidents and airbag deployments in real-time, and know where patches of ice exist on the road. Connected cars will share it to the cloud to be used by other nearby vehicles in real time, meaning that fleets of cars will learn from each other.
The first wave of semi-autonomous and ADAS vehicles are already here including Tesla's Autopilot and GM's Super Cruise Hands-Free Driving System, and fully autonomous vehicles are less than two years away.
Tech companies such as INRIX and NVIDIA are building the backend cloud services to aggregate, process, and normalize the exception-based incident data that is generated from the millions of onboard sensors that will be deployed in this space and combine it with other datasets to fully inform both vehicles and people about exception-based events on the road.
It is estimated that each fully autonomous vehicle could generate up to 4 terabytes of data per day. That's a lot of information, and being able to collect, learn from, and share it with other connected vehicles will be the key to making the roads safer and more efficient.
Of course this type of technology shift does not happen overnight. There will be years of transition where human drivers will need proactive awareness of what lies ahead long before communication is purely machine-to-machine. Whether it's an accident two miles ahead that has caused a sudden stop in interstate traffic, or a weather event that suggests decreased speeds would be judicious.
There is a need for a complete dataset that represents exceptions to normal traffic flow, or warnings specific to safety measures that should be taken. INRIX is currently consuming real-time vehicle sensor data, as well as investing in the aggregation and publication of incidents that could impede travel from many other sources.
As more sensors are placed in vehicles there will naturally be more opportunity to enhance the overall travel experience for riders. Over time vehicle sensor data, which from a driver's standpoint is actionless, is likely to replace human input crowd data relating to what's happening on the roads.
Cloud providers who are able to consume, filter, interpret, store and make sense of the vehicle data will be best positioned to add value to the connected vehicle ecosystem. Computational horsepower and very low latency transmission will be critical to informing transportation and a purpose-built platform will be needed to maximally benefit both vehicle and occupant.
The future of travel will be connected, and companies are already starting to build the services needed to support a connected future where vehicle-to-cloud and vehicle-to-vehicle (V2V) communications will enhance safety and improve the operational efficiency of the world's road network.
Originally from New Jersey, Eric is a automotive & technology reporter covering the high-tech industry in Silicon Valley. Eric has over 15 years of automotive experience and a bachelors degree in computer science. These skills, combined with technical writing and news reporting, allows him to fully understand and identify new and innovative technologies in the auto industry and beyond. He has worked at Uber on self-driving cars and as a technical writer, helping people to understand and work with technology. Outside of work, Eric likes to travel to new places, play guitar, and explore the outdoors.
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